A method of predicting the onset of wrinkling in the Yoshida Buckling Test, devised to simulate the wrinkling behavior in press-forming of sheet metal, has been developed in the present work by using an artificial neural network. The influence of different network architectures, learning parameters, and material coefficients has been investigated. The neural network was trained using data obtained by finite element analysis. The effectiveness of a neural network as a tool for predicting wrinkling limits in sheet metal-forming is examined. It is found that the trained neural network is capable of covering a wide range of material properties and its prediction of nominal strain at the onset of wrinkling is in reasonable agreement with the analytical results.